Research Fellow in Computer-aided Design and Discovery of MOFs for Adsorption Applications

Job Description

Research Fellow in Computer-aided Design and Discovery of MOFs for Adsorption Applications: Grade: 7.32 – 7.35 Salary: £47,186- £50,942 p.a. incl. London allowance

Department: Chemical Engineering: Location: London

Reports to

Principal Investigator Dr Peyman Z. Moghadam (p.moghadam@ucl.ac.uk)

Context

The UCL Department of Chemical Engineering is one of the UK's leading research and teaching departments, with an international reputation for excellence. The department offers undergraduate and postgraduate programmes and maintains a broad research portfolio spanning disciplines from the molecular scale to complex systems. It is currently undergoing a period of significant expansion, including the development of the state-of-the-art Manufacturing Futures Laboratory (MFL) research facilities at UCL East.

The MFL is a world-leading centres of research excellence, bringing together multidisciplinary research consortia to address UCL's Grand Challenges of Climate Crisis and Data-Empowered Societies. MFL leads research into the smart, rapid and flexible development of products and processes, drawing on AI, digital and automation technologies.

This project sits within UCL's growing research programme on computational materials discovery for clean-energy and separation technologies, developing predictive, data-driven design tools that shorten the path from candidate material to validated metal-organic framework (MOF) sorbent materials. It brings together expertise in molecular simulation, materials informatics and machine learning to accelerate the discovery and deployment of advanced porous materials for CO2 capture applications.

Our mission is to leverage fundamental science and the exponential growth of computational power to revolutionise industrial chemistry. We are integrating autonomous laboratories, computational modelling and AI to systematise the design, de-risking and deployment of new MOF sorbents for CO2 capture. Our work is deeply interdisciplinary and closes the loop between fundamental physics (simulations and experiments) and industrial deployment.

Main purpose of the job

The computer-aided design and discovery of metal-organic frameworks (MOFs) is central to accelerating the development of next-generation adsorbents. This project will develop and apply a computational discovery platform that combines high-throughput molecular simulation, large-scale materials databases and machine learning to identify, screen and design MOFs with optimal adsorption performance. Grand-canonical Monte Carlo (GCMC), molecular dynamics simulations, quantum calculations, automated structure generation and curation, high-throughput experimentation, and predictive machine-learning models will be integrated within a closed-loop workflow to rapidly screen vast libraries of real and hypothetical structures, understand structure-property relationships, and target the most promising candidates for experimental synthesis and validation.

As a computational chemist, you will help define the future of multi-scale modelling. You will work closely with experts in adsorption thermodynamics, process modelling and autonomous experimentation to decide how to combine modern ML and AI with rigorous physical modelling and MOF materials screening and discovery.

This is a full time, fixed-term position until 31st December 2029, funded by European Union under Horizon Europe.

Duties and responsibilities

To develop and operate high-throughput computational screening workflows for MOFs, integrating molecular simulation, automated structure generation and large-scale materials databases.

To lead computational research on the design, screening and optimisation of MOFs for target adsorption applications.

To perform advanced molecular simulations of adsorption, including quantum calculations, grand-canonical Monte Carlo (GCMC) and molecular dynamics (MD), to characterise and predict the performance of candidate materials.

To generate, curate and analyse large computational datasets, and develop machine learning models and optimisation frameworks for AI-guided MOF discovery.

To collaborate with experimental colleagues to validate computational predictions and guide the synthesis and testing of promising MOF candidates.

To contribute to the drafting and submission of papers, reports for the funder, scientific literature and intellectual property applications where appropriate.

To present key results at meetings and conferences.

To contribute to the overall activities of the research team and Department, as required.

To contribute to the induction and direction of other research staff and students, as required.

General

Follow and actively promote the UCL Ways of Working.

Contribute to Institutional citizenship activities by undertaking voluntary roles such as fire marshal or first aider; sit on committees; assist with student open days; contribute to EDI initiatives; or any other voluntary contribution to the department or across UCL.

Carry out any other duties within the scope, spirit and purpose of the job as requested by the line manager.

This job description may be reviewed and be subject to amendment in consultation with the post holder.

Person Specification

Note to job applicant: copy and paste the following criteria into your “Statement in support of your application” and describe underneath each criteria how you meet it, giving examples. You will be scored on how you meet each criteria.

Essential Criteria: Assessment method (Application form/ Interview / Practical Test)

Qualifications, experience and knowledge

Hold (or shortly expect to obtain) a Ph.D. in Machine Learning, Physics, Chemistry, Chemical Engineering, Materials Science or a related quantitative discipline (or equivalent industry experience), together with an undergraduate degree (2(i) minimum) or M.Sc. in a relevant field. Note: Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Grade 6B with payment at Grade 7 being backdated to the date of the PhD award.: Application form Demonstrable experience with atomistic/molecular simulation methods and/or high-throughput computational screening of materials.: Application form / Interview Knowledge of nanoporous materials (e.g. MOFs, zeolites or porous polymers) and adsorption processes for gas capture, with experience or knowledge of material informatics and the analysis of large materials datasets.: Application form / Interview Demonstrable experience or knowledge of computational modelling and data-driven approaches, including machine learning methods, optimisation techniques, and relevant programming skills. Experience in applying these approaches to materials, molecular, or chemical systems would be advantageous.: Application form / interview Experience with the tools and practices for developing and maintaining research software.: Application form / Interview Clear interdisciplinary communication and the curiosity to quickly learn and execute in new domains.: Application form / Interview Ability to prioritise, execute and collaborate with a high degree of agency, a willingness to be wrong, and the ambition to build something that has never existed before.: Application form / Interview Trust, rigour and an excitement to work in an interdisciplinary team at UCL alongside other computational and experimental MOF chemists.: Application form /

Skills and abilities

Research skills: Application form / Interview The ability to analyse and write up results and data: Application form / Interview The ability to present research hypothesese and results to a scientific audience: Application form /

UCL Ways of Working

Please use Ways of Working supporting indicators to clarify ‘how we work’ expectations at UCL. Please click here to find grade appropriate supporting indicators and guidance on using Ways of Working in recruitment.: Application form / Interview Contribution to institutional citizenship activities.:

A commitment to UCL’s Code of Practice on Freedom of Speech